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Month: April 2016

Nudging Pubs is the final title to a little project that Club Soda completed last year (it was called “the Dalston Burst” at the start). The final report (pdf) from the project is now out, along with a brand new website.

The aim of the project was to answer this question:

How can we encourage pubs and bars to be more welcoming to customers who want to drink less alcohol or none at all?

The report has the findings from our research and experiments, along with recommendations and key messages. And the great news is that Hackney council are funding a second year of this project, for which Club Soda has partnered with Blenheim CDP. We’ll use the Nudging Pubs website for regular updates on the project, but I’ll probably do something occasionally on this blog as well.

The American Statistical Association (ASA) has published their “statement” about p values. I have long held fairly strong views about p values, also known as “science’s dirtiest secret”, so this is exciting stuff for me. The process of drafting the ASA statement involved 20 experts, “many months” of emails, one two-day meeting, three months of draft statements, and was “lengthier and more controversial than anticipated”. The outcome is now out, in The American Statistician, with no fewer than 21 discussion notes to accompany it (mostly people involved from the start as far as I can gather).

The statement is made up of six principles, which are:

P-values can indicate how incompatible the data are with a specified statistical model.

P-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone.

Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold.

Proper inference requires full reporting and transparency.

A p-value, or statistical significance, does not measure the size of an effect or the importance of a result.

By itself, a p-value does not provide a good measure of evidence regarding a model or hypothesis.

I don’t think many people would disagree with much of this. I was expecting something a bit more radical – the principles seem fairly self-evident to me, and don’t really address the bigger issue of what to do about statistical practice. That question is addressed in the 21 comments though.

It probably says something about the topic that it needs 21 comments. And that’s also where the disagreements come in. Some note that the principles are unlikely to change anything. Some point out that the problem isn’t with p-values themselves, but the fact that they are misunderstood and abused. The Bayesians, predictably, advocate Bayes. About half say updating the teaching of statistics is the most urgent task now.

So a decent statement as far as it goes, in acknowledging the problems. But not much in the way of constructive ideas on where to go from here. Some journals have banned p-values altogether, which sounds like a knee-jerk reaction in the other extreme direction. I’d just like to see poor old p’s downgraded to one of the many statistical measures to consider when analysing data. Never the main one, and definitely not the deciding factor on whether something is important or not. I may have to wait a bit longer for that day.